13 accepted papers
Cross-domain few-shot image interpretation (CD-FSII) has been significantly advanced by fine-tuning pre-trained visual feature models using limited labeled samples in target domains. However, profound cross-domain distribution discrepancies, along with inherent conflicts between extensive object vis
Large-scale pre-trained foundation models have demonstrated remarkable generalization capabilities across diverse computer vision tasks through fine-tuning. However, existing fine-tuning approaches often encounter challenges in extreme cross-domain few-shot learning scenarios, primarily due to the s…
Cross-modality knowledge transfer aims to apply knowledge learned in the source modality to the target modality. It is more challenging than the general knowledge transfer task because of the aggravated modality shift problem due to introducing heterogeneous data. This paper proposes a novel fast cr…
Meta-learning offers a promising avenue for few-shot learning (FSL), enabling models to glean a generalizable feature embedding through episodic training on synthetic FSL tasks in a source domain. Yet, in practical scenarios where the target task diverges from that in the source domain, meta-learnin…
Image aesthetic quality assessment (AQA) aims to assign numerical aesthetic ratings to images whilst image aesthetic captioning (IAC) aims to generate textual descriptions of the aesthetic aspects of images. In this paper, we study image AQA and IAC together and present a new IAC method termed Aesth…
Prototypical Network is a popular few-shot solver that aims at establishing a feature metric generalizable to novel few-shot classification (FSC) tasks using deep neural networks. However, its performance drops dramatically when generalizing to the FSC tasks in new domains. In this study, we revisit…
Up to now, the image-based inverse tone mapping (iTM) models have been widely investigated, while there is little research on video-based iTM methods. It would be interesting to make use of these existing image-based models in the video iTM task. However, directly transferring the imagebased iTM mod…
Despite excellent progress in recent years, mode collapse remains a major unsolved problem in generative adversarial networks (GANs). In this paper, we present spectral regularization for GANs (SR-GANs), a new and robust method for combating the mode collapse problem in GANs. Theoretical analysis sh…
This paper presents a new full-reference method to assess the quality of contrast changed images. In this method, we employ a linear model to describe the relationship between local patches of reference images and contrast changed images. With parameters of this model, three quality measures conside…
In this paper, we propose a robust descriptor named as multiple gradient-related features (MGRF) in virtue of local and overall order encoding. Specifically, three types of features are introduced, including multidirectional gradient, gradient orientation, and first derivative of gradient orientatio…
In this paper, we address the problem of generating a super-resolution image based on a dictionary of low- and high-resolution exemplars from a single input image in wavelet domain with a overall enhancement procedure. Most methods extract different kinds of features in low-resolution image and high…
In this paper, we propose a fast and robust method which uses only a single frontal face image as input to reconstruct a plausible 3D face. Our method mainly consists of three stages: feature point detection, model adaptation in X-Y plane and model adjustment on Z-axis direction. At first stage, we…
Saliency detection has been a significant problem in computer vision and helpful to object detection. In this paper, we propose a new computational saliency detection model under the Bayesian framework. First, central bias and the reweighting of the salient regions in the convex hull are applied to…